Overview

Dataset statistics

Number of variables14
Number of observations160
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.8 KiB
Average record size in memory120.0 B

Variable types

Categorical1
Numeric13

Alerts

alcohol is highly overall correlated with color_intensity and 2 other fieldsHigh correlation
color_intensity is highly overall correlated with alcohol and 1 other fieldsHigh correlation
flavanoids is highly overall correlated with hue and 5 other fieldsHigh correlation
hue is highly overall correlated with flavanoids and 2 other fieldsHigh correlation
malic_acid is highly overall correlated with hue and 1 other fieldsHigh correlation
nonflavanoid_phenols is highly overall correlated with flavanoidsHigh correlation
od280/od315_of_diluted_wines is highly overall correlated with flavanoids and 3 other fieldsHigh correlation
proanthocyanins is highly overall correlated with flavanoids and 2 other fieldsHigh correlation
proline is highly overall correlated with alcohol and 1 other fieldsHigh correlation
target is highly overall correlated with alcohol and 7 other fieldsHigh correlation
total_phenols is highly overall correlated with flavanoids and 3 other fieldsHigh correlation

Reproduction

Analysis started2025-11-27 18:13:47.742953
Analysis finished2025-11-27 18:14:05.979578
Duration18.24 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

target
Categorical

High correlation 

Distinct3
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
1
64 
0
52 
2
44 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters160
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
164
40.0%
052
32.5%
244
27.5%

Length

2025-11-27T18:14:06.085892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-27T18:14:06.176115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
164
40.0%
052
32.5%
244
27.5%

Most occurring characters

ValueCountFrequency (%)
164
40.0%
052
32.5%
244
27.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)160
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
164
40.0%
052
32.5%
244
27.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)160
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
164
40.0%
052
32.5%
244
27.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)160
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
164
40.0%
052
32.5%
244
27.5%

alcohol
Real number (ℝ)

High correlation 

Distinct116
Distinct (%)72.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.998375
Minimum11.03
Maximum14.83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2025-11-27T18:14:06.284576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum11.03
5-th percentile11.755
Q112.355
median13.05
Q313.695
95-th percentile14.2205
Maximum14.83
Range3.8
Interquartile range (IQR)1.34

Descriptive statistics

Standard deviation0.80957624
Coefficient of variation (CV)0.062282881
Kurtosis-0.84440537
Mean12.998375
Median Absolute Deviation (MAD)0.68
Skewness-0.0064655254
Sum2079.74
Variance0.65541369
MonotonicityNot monotonic
2025-11-27T18:14:06.858849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.056
 
3.8%
12.375
 
3.1%
12.294
 
2.5%
12.084
 
2.5%
12.253
 
1.9%
12.423
 
1.9%
122
 
1.2%
13.582
 
1.2%
13.882
 
1.2%
13.482
 
1.2%
Other values (106)127
79.4%
ValueCountFrequency (%)
11.031
0.6%
11.451
0.6%
11.461
0.6%
11.561
0.6%
11.621
0.6%
11.641
0.6%
11.651
0.6%
11.661
0.6%
11.761
0.6%
11.791
0.6%
ValueCountFrequency (%)
14.831
0.6%
14.751
0.6%
14.391
0.6%
14.382
1.2%
14.371
0.6%
14.341
0.6%
14.231
0.6%
14.222
1.2%
14.21
0.6%
14.191
0.6%

malic_acid
Real number (ℝ)

High correlation 

Distinct121
Distinct (%)75.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3235625
Minimum0.89
Maximum5.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2025-11-27T18:14:07.013033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.89
5-th percentile1.067
Q11.6075
median1.83
Q33
95-th percentile4.6005
Maximum5.8
Range4.91
Interquartile range (IQR)1.3925

Descriptive statistics

Standard deviation1.1260117
Coefficient of variation (CV)0.48460572
Kurtosis0.47881717
Mean2.3235625
Median Absolute Deviation (MAD)0.475
Skewness1.1367992
Sum371.77
Variance1.2679023
MonotonicityNot monotonic
2025-11-27T18:14:07.162605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.737
 
4.4%
1.674
 
2.5%
1.513
 
1.9%
1.93
 
1.9%
1.533
 
1.9%
1.613
 
1.9%
1.683
 
1.9%
1.652
 
1.2%
1.772
 
1.2%
3.592
 
1.2%
Other values (111)128
80.0%
ValueCountFrequency (%)
0.891
0.6%
0.91
0.6%
0.921
0.6%
0.942
1.2%
0.981
0.6%
0.991
0.6%
1.011
0.6%
1.071
0.6%
1.091
0.6%
1.11
0.6%
ValueCountFrequency (%)
5.81
0.6%
5.651
0.6%
5.511
0.6%
5.191
0.6%
5.041
0.6%
4.951
0.6%
4.721
0.6%
4.611
0.6%
4.61
0.6%
4.431
0.6%

ash
Real number (ℝ)

Distinct78
Distinct (%)48.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.359125
Minimum1.36
Maximum3.23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2025-11-27T18:14:07.308411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.36
5-th percentile1.919
Q12.21
median2.355
Q32.5325
95-th percentile2.7515
Maximum3.23
Range1.87
Interquartile range (IQR)0.3225

Descriptive statistics

Standard deviation0.27406648
Coefficient of variation (CV)0.11617294
Kurtosis1.4463812
Mean2.359125
Median Absolute Deviation (MAD)0.155
Skewness-0.12150313
Sum377.46
Variance0.075112437
MonotonicityNot monotonic
2025-11-27T18:14:07.458476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.37
 
4.4%
2.287
 
4.4%
2.366
 
3.8%
2.326
 
3.8%
2.25
 
3.1%
2.385
 
3.1%
2.14
 
2.5%
2.484
 
2.5%
2.44
 
2.5%
2.213
 
1.9%
Other values (68)109
68.1%
ValueCountFrequency (%)
1.361
0.6%
1.72
1.2%
1.711
0.6%
1.751
0.6%
1.821
0.6%
1.881
0.6%
1.91
0.6%
1.922
1.2%
1.941
0.6%
1.951
0.6%
ValueCountFrequency (%)
3.231
0.6%
3.221
0.6%
2.921
0.6%
2.871
0.6%
2.861
0.6%
2.841
0.6%
2.81
0.6%
2.781
0.6%
2.751
0.6%
2.742
1.2%

alcalinity_of_ash
Real number (ℝ)

Distinct61
Distinct (%)38.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.430625
Minimum10.6
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2025-11-27T18:14:07.608035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10.6
5-th percentile14.57
Q117
median19.05
Q321.5
95-th percentile25
Maximum30
Range19.4
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation3.4263316
Coefficient of variation (CV)0.17633666
Kurtosis0.45440124
Mean19.430625
Median Absolute Deviation (MAD)2.25
Skewness0.25646965
Sum3108.9
Variance11.739748
MonotonicityNot monotonic
2025-11-27T18:14:07.765043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2011
 
6.9%
1810
 
6.2%
1610
 
6.2%
218
 
5.0%
198
 
5.0%
21.58
 
5.0%
227
 
4.4%
19.57
 
4.4%
18.57
 
4.4%
22.56
 
3.8%
Other values (51)78
48.8%
ValueCountFrequency (%)
10.61
0.6%
11.21
0.6%
11.41
0.6%
121
0.6%
12.41
0.6%
13.21
0.6%
142
1.2%
14.61
0.6%
14.81
0.6%
152
1.2%
ValueCountFrequency (%)
301
 
0.6%
28.52
 
1.2%
271
 
0.6%
26.51
 
0.6%
261
 
0.6%
25.51
 
0.6%
255
3.1%
24.53
1.9%
243
1.9%
23.61
 
0.6%

magnesium
Real number (ℝ)

Distinct52
Distinct (%)32.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.03125
Minimum70
Maximum162
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2025-11-27T18:14:07.918624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile83.9
Q188
median98
Q3107
95-th percentile126.05
Maximum162
Range92
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.30768
Coefficient of variation (CV)0.1430321
Kurtosis2.3686521
Mean100.03125
Median Absolute Deviation (MAD)9.5
Skewness1.1724946
Sum16005
Variance204.70971
MonotonicityNot monotonic
2025-11-27T18:14:08.074338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8811
 
6.9%
8611
 
6.9%
989
 
5.6%
1018
 
5.0%
1026
 
3.8%
966
 
3.8%
856
 
3.8%
925
 
3.1%
1035
 
3.1%
975
 
3.1%
Other values (42)88
55.0%
ValueCountFrequency (%)
701
 
0.6%
781
 
0.6%
804
 
2.5%
811
 
0.6%
821
 
0.6%
843
 
1.9%
856
3.8%
8611
6.9%
872
 
1.2%
8811
6.9%
ValueCountFrequency (%)
1621
0.6%
1511
0.6%
1391
0.6%
1361
0.6%
1341
0.6%
1321
0.6%
1281
0.6%
1271
0.6%
1261
0.6%
1241
0.6%

total_phenols
Real number (ℝ)

High correlation 

Distinct93
Distinct (%)58.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.298125
Minimum0.98
Maximum3.88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2025-11-27T18:14:08.222534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.98
5-th percentile1.38
Q11.735
median2.335
Q32.8
95-th percentile3.3
Maximum3.88
Range2.9
Interquartile range (IQR)1.065

Descriptive statistics

Standard deviation0.63617982
Coefficient of variation (CV)0.2768256
Kurtosis-0.83841997
Mean2.298125
Median Absolute Deviation (MAD)0.53
Skewness0.11029783
Sum367.7
Variance0.40472476
MonotonicityNot monotonic
2025-11-27T18:14:08.372624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.28
 
5.0%
2.66
 
3.8%
35
 
3.1%
2.955
 
3.1%
2.85
 
3.1%
1.384
 
2.5%
1.654
 
2.5%
2.423
 
1.9%
2.53
 
1.9%
1.73
 
1.9%
Other values (83)114
71.2%
ValueCountFrequency (%)
0.981
 
0.6%
1.11
 
0.6%
1.151
 
0.6%
1.251
 
0.6%
1.281
 
0.6%
1.31
 
0.6%
1.351
 
0.6%
1.384
2.5%
1.392
1.2%
1.411
 
0.6%
ValueCountFrequency (%)
3.881
 
0.6%
3.851
 
0.6%
3.521
 
0.6%
3.51
 
0.6%
3.41
 
0.6%
3.381
 
0.6%
3.33
1.9%
3.271
 
0.6%
3.252
1.2%
3.21
 
0.6%

flavanoids
Real number (ℝ)

High correlation 

Distinct123
Distinct (%)76.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0246875
Minimum0.34
Maximum5.08
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2025-11-27T18:14:08.517278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.34
5-th percentile0.5595
Q11.0975
median2.135
Q32.865
95-th percentile3.541
Maximum5.08
Range4.74
Interquartile range (IQR)1.7675

Descriptive statistics

Standard deviation1.0112816
Coefficient of variation (CV)0.4994754
Kurtosis-0.87531552
Mean2.0246875
Median Absolute Deviation (MAD)0.845
Skewness0.063525896
Sum323.95
Variance1.0226905
MonotonicityNot monotonic
2025-11-27T18:14:08.654565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.583
 
1.9%
2.033
 
1.9%
0.63
 
1.9%
2.653
 
1.9%
2.172
 
1.2%
2.992
 
1.2%
32
 
1.2%
3.392
 
1.2%
2.682
 
1.2%
1.592
 
1.2%
Other values (113)136
85.0%
ValueCountFrequency (%)
0.341
 
0.6%
0.472
1.2%
0.481
 
0.6%
0.491
 
0.6%
0.511
 
0.6%
0.521
 
0.6%
0.551
 
0.6%
0.561
 
0.6%
0.571
 
0.6%
0.583
1.9%
ValueCountFrequency (%)
5.081
0.6%
3.931
0.6%
3.751
0.6%
3.741
0.6%
3.691
0.6%
3.671
0.6%
3.641
0.6%
3.561
0.6%
3.541
0.6%
3.491
0.6%

nonflavanoid_phenols
Real number (ℝ)

High correlation 

Distinct38
Distinct (%)23.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3655
Minimum0.13
Maximum0.66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2025-11-27T18:14:08.779709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.13
5-th percentile0.19
Q10.26
median0.34
Q30.455
95-th percentile0.6005
Maximum0.66
Range0.53
Interquartile range (IQR)0.195

Descriptive statistics

Standard deviation0.12755465
Coefficient of variation (CV)0.34898673
Kurtosis-0.73897963
Mean0.3655
Median Absolute Deviation (MAD)0.09
Skewness0.40580911
Sum58.48
Variance0.016270189
MonotonicityNot monotonic
2025-11-27T18:14:08.923340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0.2610
 
6.2%
0.439
 
5.6%
0.329
 
5.6%
0.348
 
5.0%
0.298
 
5.0%
0.47
 
4.4%
0.247
 
4.4%
0.536
 
3.8%
0.276
 
3.8%
0.36
 
3.8%
Other values (28)84
52.5%
ValueCountFrequency (%)
0.131
 
0.6%
0.142
 
1.2%
0.174
 
2.5%
0.192
 
1.2%
0.22
 
1.2%
0.216
3.8%
0.225
3.1%
0.247
4.4%
0.252
 
1.2%
0.2610
6.2%
ValueCountFrequency (%)
0.661
 
0.6%
0.634
2.5%
0.613
1.9%
0.63
1.9%
0.583
1.9%
0.561
 
0.6%
0.551
 
0.6%
0.536
3.8%
0.525
3.1%
0.55
3.1%

proanthocyanins
Real number (ℝ)

High correlation 

Distinct97
Distinct (%)60.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.606125
Minimum0.41
Maximum3.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2025-11-27T18:14:09.059601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.41
5-th percentile0.73
Q11.25
median1.56
Q31.9625
95-th percentile2.7625
Maximum3.58
Range3.17
Interquartile range (IQR)0.7125

Descriptive statistics

Standard deviation0.57898087
Coefficient of variation (CV)0.36048307
Kurtosis0.56582605
Mean1.606125
Median Absolute Deviation (MAD)0.39
Skewness0.50705353
Sum256.98
Variance0.33521885
MonotonicityNot monotonic
2025-11-27T18:14:09.210355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.359
 
5.6%
1.466
 
3.8%
1.875
 
3.1%
1.564
 
2.5%
2.084
 
2.5%
1.984
 
2.5%
1.773
 
1.9%
1.143
 
1.9%
1.253
 
1.9%
2.813
 
1.9%
Other values (87)116
72.5%
ValueCountFrequency (%)
0.411
0.6%
0.422
1.2%
0.551
0.6%
0.621
0.6%
0.641
0.6%
0.681
0.6%
0.732
1.2%
0.82
1.2%
0.811
0.6%
0.832
1.2%
ValueCountFrequency (%)
3.581
 
0.6%
3.281
 
0.6%
2.961
 
0.6%
2.912
1.2%
2.813
1.9%
2.761
 
0.6%
2.71
 
0.6%
2.51
 
0.6%
2.451
 
0.6%
2.382
1.2%

color_intensity
Real number (ℝ)

High correlation 

Distinct122
Distinct (%)76.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1100625
Minimum1.74
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2025-11-27T18:14:09.355623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.74
5-th percentile2.1485
Q13.24
median4.75
Q36.2625
95-th percentile9.7099999
Maximum13
Range11.26
Interquartile range (IQR)3.0225

Descriptive statistics

Standard deviation2.350731
Coefficient of variation (CV)0.46002002
Kurtosis0.3525014
Mean5.1100625
Median Absolute Deviation (MAD)1.52
Skewness0.89453658
Sum817.61
Variance5.5259364
MonotonicityNot monotonic
2025-11-27T18:14:09.499725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.84
 
2.5%
2.64
 
2.5%
4.53
 
1.9%
53
 
1.9%
2.93
 
1.9%
3.053
 
1.9%
5.43
 
1.9%
4.63
 
1.9%
2.83
 
1.9%
2.52
 
1.2%
Other values (112)129
80.6%
ValueCountFrequency (%)
1.741
0.6%
1.91
0.6%
1.952
1.2%
2.062
1.2%
2.081
0.6%
2.121
0.6%
2.151
0.6%
2.31
0.6%
2.41
0.6%
2.452
1.2%
ValueCountFrequency (%)
131
0.6%
11.751
0.6%
10.81
0.6%
10.681
0.6%
10.521
0.6%
10.261
0.6%
10.21
0.6%
9.8999991
0.6%
9.71
0.6%
9.581
0.6%

hue
Real number (ℝ)

High correlation 

Distinct76
Distinct (%)47.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9577875
Minimum0.48
Maximum1.71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2025-11-27T18:14:09.641166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.48
5-th percentile0.57
Q10.7875
median0.98
Q31.12
95-th percentile1.2815
Maximum1.71
Range1.23
Interquartile range (IQR)0.3325

Descriptive statistics

Standard deviation0.23115968
Coefficient of variation (CV)0.24134756
Kurtosis-0.31143652
Mean0.9577875
Median Absolute Deviation (MAD)0.165
Skewness0.021126667
Sum153.246
Variance0.053434797
MonotonicityNot monotonic
2025-11-27T18:14:09.785848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.048
 
5.0%
1.237
 
4.4%
1.255
 
3.1%
0.575
 
3.1%
0.895
 
3.1%
0.754
 
2.5%
1.094
 
2.5%
1.054
 
2.5%
0.864
 
2.5%
1.124
 
2.5%
Other values (66)110
68.8%
ValueCountFrequency (%)
0.481
 
0.6%
0.541
 
0.6%
0.551
 
0.6%
0.562
 
1.2%
0.575
3.1%
0.582
 
1.2%
0.591
 
0.6%
0.63
1.9%
0.612
 
1.2%
0.621
 
0.6%
ValueCountFrequency (%)
1.711
 
0.6%
1.451
 
0.6%
1.421
 
0.6%
1.381
 
0.6%
1.362
 
1.2%
1.331
 
0.6%
1.311
 
0.6%
1.281
 
0.6%
1.271
 
0.6%
1.255
3.1%

od280/od315_of_diluted_wines
Real number (ℝ)

High correlation 

Distinct113
Distinct (%)70.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5985625
Minimum1.27
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2025-11-27T18:14:09.927702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.27
5-th percentile1.4675
Q11.8525
median2.78
Q33.17
95-th percentile3.58
Maximum4
Range2.73
Interquartile range (IQR)1.3175

Descriptive statistics

Standard deviation0.71703868
Coefficient of variation (CV)0.27593667
Kurtosis-1.1617567
Mean2.5985625
Median Absolute Deviation (MAD)0.54
Skewness-0.27782802
Sum415.77
Variance0.51414446
MonotonicityNot monotonic
2025-11-27T18:14:10.084516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.875
 
3.1%
1.824
 
2.5%
2.773
 
1.9%
1.333
 
1.9%
33
 
1.9%
2.963
 
1.9%
1.753
 
1.9%
2.783
 
1.9%
3.173
 
1.9%
2.062
 
1.2%
Other values (103)128
80.0%
ValueCountFrequency (%)
1.271
 
0.6%
1.292
1.2%
1.333
1.9%
1.361
 
0.6%
1.421
 
0.6%
1.471
 
0.6%
1.481
 
0.6%
1.512
1.2%
1.551
 
0.6%
1.562
1.2%
ValueCountFrequency (%)
41
0.6%
3.921
0.6%
3.711
0.6%
3.691
0.6%
3.641
0.6%
3.631
0.6%
3.591
0.6%
3.582
1.2%
3.571
0.6%
3.561
0.6%

proline
Real number (ℝ)

High correlation 

Distinct114
Distinct (%)71.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean744.5125
Minimum278
Maximum1680
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2025-11-27T18:14:10.220955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum278
5-th percentile354.85
Q1498.75
median666
Q3985
95-th percentile1296.25
Maximum1680
Range1402
Interquartile range (IQR)486.25

Descriptive statistics

Standard deviation315.62803
Coefficient of variation (CV)0.4239392
Kurtosis-0.1621552
Mean744.5125
Median Absolute Deviation (MAD)200.5
Skewness0.80186405
Sum119122
Variance99621.056
MonotonicityNot monotonic
2025-11-27T18:14:10.363886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5205
 
3.1%
6254
 
2.5%
6804
 
2.5%
4503
 
1.9%
6603
 
1.9%
4803
 
1.9%
4953
 
1.9%
6303
 
1.9%
5623
 
1.9%
7503
 
1.9%
Other values (104)126
78.8%
ValueCountFrequency (%)
2781
0.6%
2901
0.6%
3121
0.6%
3151
0.6%
3421
0.6%
3452
1.2%
3521
0.6%
3551
0.6%
3651
0.6%
3781
0.6%
ValueCountFrequency (%)
16801
0.6%
15471
0.6%
15151
0.6%
15101
0.6%
14801
0.6%
14501
0.6%
13751
0.6%
13201
0.6%
12951
0.6%
12901
0.6%

Interactions

2025-11-27T18:14:04.457960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T18:13:48.142020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T18:13:49.700849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T18:13:50.826566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T18:13:52.058308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T18:13:53.356291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T18:13:54.481322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T18:13:55.704795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T18:13:56.816016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T18:13:58.394143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T18:13:59.598499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T18:14:00.808495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T18:14:03.162025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-27T18:13:56.560147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T18:13:58.131380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T18:13:59.321375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T18:14:00.537082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T18:14:02.564928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T18:14:04.174394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T18:14:05.472219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T18:13:49.256369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T18:13:50.656022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T18:13:51.858701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T18:13:53.154355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T18:13:54.307517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T18:13:55.524279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T18:13:56.641840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T18:13:58.220551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T18:13:59.410537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T18:14:00.626094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T18:14:02.762396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T18:14:04.264425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T18:14:05.563296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T18:13:49.356729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T18:13:50.740647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T18:13:51.955833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T18:13:53.253183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T18:13:54.393208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T18:13:55.614234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T18:13:56.728462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T18:13:58.306853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T18:13:59.505534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T18:14:00.717503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T18:14:02.952878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-27T18:14:04.358051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-27T18:14:10.483879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
alcalinity_of_ashalcoholashcolor_intensityflavanoidshuemagnesiummalic_acidnonflavanoid_phenolsod280/od315_of_diluted_winesproanthocyaninsprolinetargettotal_phenols
alcalinity_of_ash1.000-0.3280.360-0.083-0.462-0.387-0.1780.3590.412-0.317-0.278-0.4930.406-0.395
alcohol-0.3281.0000.2370.6250.286-0.0180.3190.101-0.1490.1080.2120.6260.5660.314
ash0.3600.2371.0000.2800.056-0.0900.3270.2950.1630.005-0.0090.2260.1910.116
color_intensity-0.0830.6250.2801.000-0.056-0.4430.3100.2970.074-0.317-0.0260.4530.6460.011
flavanoids-0.4620.2860.056-0.0561.0000.5340.216-0.330-0.5440.7580.7320.4120.7550.873
hue-0.387-0.018-0.090-0.4430.5341.0000.054-0.537-0.2820.4910.3300.1940.5860.424
magnesium-0.1780.3190.3270.3100.2160.0541.0000.051-0.2340.0770.1580.4990.3720.236
malic_acid0.3590.1010.2950.297-0.330-0.5370.0511.0000.281-0.257-0.231-0.0670.508-0.276
nonflavanoid_phenols0.412-0.1490.1630.074-0.544-0.282-0.2340.2811.000-0.496-0.402-0.2750.350-0.455
od280/od315_of_diluted_wines-0.3170.1080.005-0.3170.7580.4910.077-0.257-0.4961.0000.5780.2450.6540.697
proanthocyanins-0.2780.212-0.009-0.0260.7320.3300.158-0.231-0.4020.5781.0000.2930.4230.667
proline-0.4930.6260.2260.4530.4120.1940.499-0.067-0.2750.2450.2931.0000.6400.404
target0.4060.5660.1910.6460.7550.5860.3720.5080.3500.6540.4230.6401.0000.537
total_phenols-0.3950.3140.1160.0110.8730.4240.236-0.276-0.4550.6970.6670.4040.5371.000

Missing values

2025-11-27T18:14:05.706965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-27T18:14:05.847656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

targetalcoholmalic_acidashalcalinity_of_ashmagnesiumtotal_phenolsflavanoidsnonflavanoid_phenolsproanthocyaninscolor_intensityhueod280/od315_of_diluted_winesproline
9013.861.352.2716.0982.983.150.221.857.2200001.013.551045
114112.081.392.5022.5842.562.290.431.042.9000000.933.19385
18014.191.592.4816.51083.303.930.321.868.7000001.232.821680
66113.111.011.7015.0782.983.180.262.285.3000001.123.18502
60112.331.102.2816.01012.051.090.630.413.2700001.251.67680
169213.404.602.8625.01121.980.960.271.118.5000000.671.92630
171212.772.392.2819.5861.390.510.480.649.8999990.571.63470
164213.782.762.3022.0901.350.680.411.039.5800000.701.68615
117112.421.612.1922.51082.002.090.341.612.0600001.062.96345
65112.371.212.5618.1982.422.650.372.084.6000001.192.30678
targetalcoholmalic_acidashalcalinity_of_ashmagnesiumtotal_phenolsflavanoidsnonflavanoid_phenolsproanthocyaninscolor_intensityhueod280/od315_of_diluted_winesproline
87111.651.672.6226.0881.921.610.401.342.601.363.21562
74111.961.092.3021.01013.382.140.131.653.210.993.13886
121111.562.053.2328.51193.185.080.471.876.000.933.69465
177214.134.102.7424.5962.050.760.561.359.200.611.60560
20014.061.632.2816.01263.003.170.242.105.651.093.71780
71113.861.512.6725.0862.952.860.211.873.381.363.16410
106112.251.732.1219.0801.652.030.371.633.401.003.17510
14014.381.872.3812.01023.303.640.292.967.501.203.001547
92112.691.532.2620.7801.381.460.581.623.050.962.06495
102112.342.452.4621.0982.562.110.341.312.800.803.38438